SEQ CHESTX#

Classes#

class datasets.seq_chestx.ChestX(root, train=True, transform=None, target_transform=None, download=False)[source]#

Bases: Dataset

LABELS = ['Cardiomegaly', 'Consolidation', 'Edema', 'Fibrosis', 'Pleural Thickening', 'Pneumothorax']#: Overrides the ChestX dataset to change the getitem function.

N_CLASSES = 6#: To reduce the effect of the severe imbalance in the dataset, we drop the two classes with the smallest and largest amount of samples.

class datasets.seq_chestx.SequentialChestX(args)[source]#

Bases: ContinualDataset

MEAN = [0.485, 0.456, 0.406]#

NAME: str = 'seq-chestx'#

N_CLASSES: int = 6#

N_CLASSES_PER_TASK: int = 3#

N_TASKS: int = 2#

SETTING: str = 'class-il'#

SIZE: Tuple[int] = (224, 224)#

STD = [0.229, 0.224, 0.225]#

TEST_TRANSFORM = Compose( Resize(size=(224, 224), interpolation=bicubic, max_size=None, antialias=True) ToTensor() Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )#

TRANSFORM = Compose( Resize(size=(224, 224), interpolation=bicubic, max_size=None, antialias=True) ToTensor() Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) )#

get_backbone()[source]#

get_batch_size()[source]#

get_class_names()[source]#

get_data_loaders()[source]#

static get_denormalization_transform()[source]#

get_epochs()[source]#

static get_loss()[source]#

static get_normalization_transform()[source]#

static get_prompt_templates()[source]#

static get_transform()[source]#

normalize = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])#